Optimal tracking control for slag grinding process based on adaptive dynamic programming - Details

Author：

Wang, Kang (Wang, Kang.) | Li, Xiao-Li (Li, Xiao-Li.) (Scholars：李晓理) | Jia, Chao (Jia, Chao.) | Song, Gui-Zhi (Song, Gui-Zhi.)

Indexed by：

EI Scopus PKU CSCD

Abstract：

Super　fine　slag　powder　is　a　new　kind　of　green　environmental-friendly　construction　material,　which　can　greatly　improve　the　mechanical　properties　of　cement　concrete.　However,　the　slag　powder　grinding　process　is　hard　to　identify　by　a　mechanism　model.　In　this　paper,　a　data-driven　based　recurrent　neural　network　model　is　constructed　utilizing　the　information　measured　from　slag　grinding　system.　Based　on　this　model,　an　adaptive　dynamic　programming　algorithm　is　proposed　to　realize　the　optimal　tracking　control　with　constrained　control　input.　Further,　this　algorithm　is　applied　to　the　slag　grinding　process.　Simulation　examples　show　that　the　data-based　model　can　effectively　identify　the　grinding　process,　and　the　control　method　can　realize　the　optimal　tracking　control　of　specific　surface　area　and　mill　differential　pressure　with　control　constraints.　Copyright　©　2016　Acta　Automatica　Sinica.　All　rights　reserved.

Keyword：

Dynamic programming Navigation Process control Grinding (machining) Dynamics Slags Recurrent neural networks

Author Community：

[ 1 ] [Wang, Kang]School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing; 100083, China
[ 2 ] [Li, Xiao-Li]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Jia, Chao]School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing; 100083, China
[ 4 ] [Song, Gui-Zhi]Jinan Luxin Materials Company Limited, Jinan; 250109, China